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dc.contributor.advisorCarrasco-Navarro, Rocío
dc.contributor.authorSánchez-Martínez, César A.
dc.date.accessioned2021-07-16T23:04:32Z
dc.date.available2021-07-16T23:04:32Z
dc.date.issued2020-12
dc.identifier.citationSánchez-Martínez, C. A. (2020). Machine Learning Techniques for Electrical Validation Enhancement Processes. Trabajo de obtención de grado, Maestría en Ciencia de Datos. Tlaquepaque, Jalisco: ITESOes_MX
dc.identifier.urihttps://hdl.handle.net/11117/7451
dc.descriptionPost-Silicon system margin validation consumes a significant amount of time and resources. To overcome this, a reduced validation plan for derivative products has previously been used. However, a certain amount of validation is still needed to avoid escapes, which is prone to subjective bias by the validation engineer comparing a reduced set of derivative validation data against the base product data. Machine Learning techniques allow, to perform automatic decisions and predictions based on already available historical data. In this work, we present an efficient methodology implemented with Machine Learning to make an automatic risk assessment decision and eye margin estimation measurements for derivative products, considering a large set of parameters obtained from the base product. The proposed methodology yields a high performance on the risk assessment decision and the estimation by regression, which translates into a significant reduction in time, effort, and resources.es_MX
dc.language.isoenges_MX
dc.publisherITESOes_MX
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdfes_MX
dc.subjectElectrical Validationes_MX
dc.subjectMachine Learninges_MX
dc.subjectSupport Vector Machinees_MX
dc.subjectDecision Treees_MX
dc.subjectArtificial Neural Networkes_MX
dc.subjectLogites_MX
dc.subjectElectrical Testinges_MX
dc.subjectLinear Regressiones_MX
dc.subjectEqualizeres_MX
dc.subjectSystem Marginality Validationes_MX
dc.subjectSATAes_MX
dc.subjectPost-Silicon Validationes_MX
dc.titleMachine Learning Techniques for Electrical Validation Enhancement Processeses_MX
dc.typeinfo:eu-repo/semantics/masterThesises_MX
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones_MX


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